Gánti's chemoton model is an illustrious example of a minimal cell model. It is composed of three stoichiometrically coupled autocatalytic subsystems: a metabolism, a template replication process, and a membrane enclosing the other two. Earlier studies on chemoton dynamics yield inconsistent results. Furthermore, they all appealed to deterministic simulations, which do not take into account the stochastic effects induced by small population sizes. We present, for the first time, results of a chemoton simulation in which these stochastic effects have been taken into account. We investigate the dynamics of the system and analyze in depth the mechanisms responsible for the observed behavior. Our results suggest that, in contrast to the most recent study by Munteanu and Solé, the stochastic chemoton reaches a unique stable division time after a short transient phase. We confirm the existence of an optimal template length and show that this is a consequence of the monomer concentration, which depends on the template length and the initiation threshold. Since longer templates imply shorter division times, these results motivate the selective pressure toward longer templates observed in nature.

A coevolutionary model is discussed that incorporates the logical structure of constitutional chemistry and its kinetics on the one hand and the topological evolution of the chemical reaction network on the other hand. The motivation for designing this model is twofold. First, experiments that are to provide insight into chemical problems should be expressed in a syntax that remains as close as possible to real chemistry. Second, the study of physical properties of the complex chemical reaction networks requires growing models that incorporate features realistic from a biochemical perspective. In this article the theory and algorithms underlying the coevolutionary model are explained, and two illustrative examples are provided. These examples show that one needs to be careful in making general claims concerning the structure of chemical reaction networks.

The DNA of some naturally competent species of bacteria contains a large number of evenly distributed copies of a short sequence. This highly overrepresented sequence is believed to be an uptake signal sequence (USS) that helps bacteria to take up DNA selectively from (dead) members of their own species. For some time it has been assumed that the USS evolved in order to enable bacteria to distinguish between conspecific and nonconspecific DNA fragments (the preference-first hypothesis). Recently, Redfield suggested that this hypothesis is not in fact realistic, as it would require biologically implausible group selection. In this article we present a model designed to demonstrate the emergence of similar USSs in a population of simulated evolving agents. We use this model to examine the conditions under which a USS will emerge in a preference-first scenario.